TY - CONF A1 - Gutbrod, Max A1 - Geisler, Benedikt A1 - Rauber, David A1 - Palm, Christoph A2 - Maier, Andreas A2 - Deserno, Thomas M. A2 - Handels, Heinz A2 - Maier-Hein, Klaus A2 - Palm, Christoph A2 - Tolxdorff, Thomas T1 - Data Augmentation for Images of Chronic Foot Wounds T2 - Bildverarbeitung für die Medizin 2024: Proceedings, German Workshop on Medical Image Computing, March 10-12, 2024, Erlangen N2 - Training data for Neural Networks is often scarce in the medical domain, which often results in models that struggle to generalize and consequently showpoor performance on unseen datasets. Generally, adding augmentation methods to the training pipeline considerably enhances a model’s performance. Using the dataset of the Foot Ulcer Segmentation Challenge, we analyze two additional augmentation methods in the domain of chronic foot wounds - local warping of wound edges along with projection and blurring of shapes inside wounds. Our experiments show that improvements in the Dice similarity coefficient and Normalized Surface Distance metrics depend on a sensible selection of those augmentation methods. Y1 - 2024 UR - https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/docId/7116 SP - 261 EP - 266 PB - Springer CY - Wiesbaden ER -